Audience Measurement System gauges reactions to digital signage.
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Press Release Summary:
Digital signage viewer counter utilizes images from IP camera for analysis with FACE U human face identification algorithms. It records how many people look at display and for how long, and transmits these real-time data to content management system of digital signage network. Able to verify audience viewing distance from up to 6.5 m, product can also synchronize data with content to determine how many people saw each advertisement.
Original Press Release:
EcafTech Introduces a New Audience Measurement Solution for Digital Signage - DSVC
EcafTech a Taiwan company that develops software focused on the audience measurement in Digital Signage (DS), in September 2010 announced its leading audience measurement solution for out-of-home media-Digital signage viewer counter (DSVC).
In the modern age, traditional media can only achieve audience information with its sampled monitoring and sampled survey methods. Instead of relying on sampled responses from viewers, DSVC adopts the new generation biometric technology, the FACE U human face identification algorithms. Working with a single IP CAM, the DSVC quickly and precisely captures the people who walk near and records the people who actually look at your DS. It continually transmits the real-time data to the Content Management System of your DS network. The real-time data can be further synchronized with contents to determine how many people saw each ad being played. These entirely anonymous, meaningful and powerful metrics help to evaluate the media performance accurately and to decide the best location for DS.
DSVC's outstanding features included:
This release, offers the best-in-class answer for DS system integrators and digital Out-Of-Home Media companies, it increases the reliability of the Presence, Notice, and Dwell Time measures beyond what is achievable in methods currently used with traditional media.
About EcafTech
EcafTech has devoted itself into the research of biometric identification technology since 2005 and we integrate our unique face recognition algorithms into the development of application products. In 2010 we have the powerful Face U host that utilizes the combination of two main recognition algorithms, the Principal Component Analysis with eigenface and Linear Discriminate Analysis, to achieve the fast and accurate recognition of each individual face.